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Improving SeNA-CNN by Automating Task Recognition

dc.contributor.authorZacarias, Abel
dc.contributor.authorAlexandre, Luís
dc.date.accessioned2020-01-09T10:37:01Z
dc.date.available2020-01-09T10:37:01Z
dc.date.issued2018
dc.description.abstractCatastrophic forgetting arises when a neural network is not capable of preserving the past learned task when learning a new task. There are already some methods proposed to mitigate this problem in arti cial neural networks. In this paper we propose to improve upon our previous state-of-the-art method, SeNA-CNN, such as to enable the automatic recognition in test time of the task to be solved and we experimentally show that it has excellent results. The experiments show the learning of up to 4 di erent tasks with a single network, without forgetting how to solve previous learned tasks.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-03493-1_74pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8145
dc.language.isoengpt_PT
dc.subjectSupervised Learningpt_PT
dc.subjectLifelong learningpt_PT
dc.subjectCatastrophic Forgettingpt_PT
dc.subjectConvolutional Neural Networkspt_PT
dc.titleImproving SeNA-CNN by Automating Task Recognitionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage721pt_PT
oaire.citation.startPage711pt_PT
oaire.citation.volume11314pt_PT
person.familyNameZacarias
person.familyNameAlexandre
person.givenNameAbel
person.givenNameLuís
person.identifier.ciencia-id7612-6C59-2F02
person.identifier.ciencia-id2014-0F06-A3E3
person.identifier.orcid0000-0002-0226-9682
person.identifier.orcid0000-0002-5133-5025
person.identifier.ridE-8770-2013
person.identifier.scopus-author-id8847713100
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication9f46b558-e59e-4c92-95ac-7f3f06b0ed16
relation.isAuthorOfPublication131ec6eb-b61a-4f27-953f-12e948a43a96
relation.isAuthorOfPublication.latestForDiscovery131ec6eb-b61a-4f27-953f-12e948a43a96

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